{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "聚类模型是试图检测未标记数据中模式的无监督方法。聚类算法主要有两类：聚集聚类将相似的数据点连接在一起，而质心聚类则试图在数据中找到中心或分区。Yellowbrick提供yellowbrick.cluster用于可视化和评估群集行为的模块。目前，我们提供了几种可视化工具来评估质心机制，特别是K均值聚类，帮助我们发现聚类度量中的最佳K参数：\n",
    "+ Elbow Method：根据某个评分函数对聚类进行可视化，在曲线中寻找“Elbow”。\n",
    "+ Silhouette Visualize：在一个模型中可视化每个集群的轮廓分数。\n",
    "+ Intercluster Distance：可视化簇的相对距离和大小。\n",
    "\n",
    "本文如果数据集下载不下来，查看下面地址，然后放入yellowbrick安装目录\\datasets\\fixtures文件夹:\n",
    "\n",
    "\n",
    "```\n",
    "{\n",
    "  \"bikeshare\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/bikeshare.zip\",\n",
    "    \"signature\": \"4ed07a929ccbe0171309129e6adda1c4390190385dd6001ba9eecc795a21eef2\"\n",
    "  },\n",
    "  \"hobbies\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/hobbies.zip\",\n",
    "    \"signature\": \"6114e32f46baddf049a18fb05bad3efa98f4e6a0fe87066c94071541cb1e906f\"\n",
    "  },\n",
    "  \"concrete\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/concrete.zip\",\n",
    "    \"signature\": \"5807af2f04e14e407f61e66a4f3daf910361a99bb5052809096b47d3cccdfc0a\"\n",
    "  },\n",
    "  \"credit\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/credit.zip\",\n",
    "    \"signature\": \"2c6f5821c4039d70e901cc079d1404f6f49c3d6815871231c40348a69ae26573\"\n",
    "  },\n",
    "  \"energy\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/energy.zip\",\n",
    "    \"signature\": \"174eca3cd81e888fc416c006de77dbe5f89d643b20319902a0362e2f1972a34e\"\n",
    "  },\n",
    "  \"game\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/game.zip\",\n",
    "    \"signature\": \"ce799d1c55fcf1985a02def4d85672ac86c022f8f7afefbe42b20364fba47d7a\"\n",
    "  },\n",
    "  \"mushroom\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/mushroom.zip\",\n",
    "    \"signature\": \"f79fdbc33b012dabd06a8f3cb3007d244b6aab22d41358b9aeda74417c91f300\"\n",
    "  },\n",
    "  \"occupancy\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/occupancy.zip\",\n",
    "    \"signature\": \"0b390387584586a05f45c7da610fdaaf8922c5954834f323ae349137394e6253\"\n",
    "  },\n",
    "  \"spam\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/spam.zip\",\n",
    "    \"signature\": \"000309ac2b61090a3001de3e262a5f5319708bb42791c62d15a08a2f9f7cb30a\"\n",
    "  },\n",
    "  \"walking\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/walking.zip\",\n",
    "    \"signature\": \"7a36615978bc3bb74a2e9d5de216815621bd37f6a42c65d3fc28b242b4d6e040\"\n",
    "  },\n",
    "  \"nfl\": {\n",
    "    \"url\": \"https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/nfl.zip\",\n",
    "    \"signature\": \"4989c66818ea18217ee0fe3a59932b963bd65869928c14075a5c50366cb81e1f\"\n",
    "  }\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 多行输出\r\n",
    "from IPython.core.interactiveshell import InteractiveShell\r\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 1 Elbow Method\n",
    "KElbowVisualizer实现了“肘部”方法，以帮助数据科学家通过用K的一系列值拟合模型来选择最佳的簇数。如果折线图类似于手臂，则“肘部”（曲线上的拐点）是一个很好的指示，表明基础模型在该点最适合。在可视化工具中，“弯头”将用虚线注释。\n",
    "\n",
    "|可视化器|KElbowVisualizer|\n",
    "|-|-|\n",
    "|快速使用方法|kelbow_visualizer()|\n",
    "|模型|聚类|\n",
    "|工作流程|模型评估|\n",
    "\n",
    "为了证明这一点，在下面的例子中，KElbowVisualizer在一个包含8个随机点簇的二维数据集上拟合KMeans模型，该模型的K值范围从4到11。当模型适合8个簇时，我们可以看到一条线在图中注释“弯头”，在本例中我们知道这是最佳数目。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.1 基础使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\r\n",
    "from sklearn.datasets import make_blobs\r\n",
    "\r\n",
    "from yellowbrick.cluster import KElbowVisualizer\r\n",
    "\r\n",
    "# Generate synthetic dataset with 8 random clusters\r\n",
    "# 建立具有8个随机点簇中心的数据\r\n",
    "X, y = make_blobs(n_samples=1000, n_features=12, centers=8, random_state=42)\r\n",
    "\r\n",
    "# Instantiate the clustering model and visualizer\r\n",
    "model = KMeans()\r\n",
    "# 可视化\r\n",
    "visualizer = KElbowVisualizer(model, k=(4,12))\r\n",
    "\r\n",
    "visualizer.fit(X)        # Fit the data to the visualizer\r\n",
    "visualizer.show();        # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "默认情况下，评分参数度量metric设置为distortion，这将计算从每个点到其指定中心的平方距离之和。\n",
    "但是，KElbowVisualizer还可以使用另外两个指标-sihouette和calinski_harabasz。\n",
    "silhouette计算所有采样的平均轮廓系数，而calinski_harabasz分数计算簇之间和簇内的分散率。\n",
    "\n",
    "KElbowVisualizer还将每K训练聚类模型的时间量显示为一条绿色虚线，但可以通过设置timings=False来隐藏。在下面的示例中，我们将使用calinski_harabasz分数并隐藏时间以适合模型。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\r\n",
    "from sklearn.datasets import make_blobs\r\n",
    "\r\n",
    "from yellowbrick.cluster import KElbowVisualizer\r\n",
    "\r\n",
    "# Generate synthetic dataset with 8 random clusters\r\n",
    "X, y = make_blobs(n_samples=1000, n_features=12, centers=8, random_state=42)\r\n",
    "\r\n",
    "# Instantiate the clustering model and visualizer\r\n",
    "model = KMeans()\r\n",
    "visualizer = KElbowVisualizer(\r\n",
    "    model, k=(4,12), metric='calinski_harabasz', timings=False\r\n",
    ")\r\n",
    "\r\n",
    "visualizer.fit(X)        # Fit the data to the visualizer\r\n",
    "visualizer.show();        # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "默认情况下，参数locate_elbow设置为True，使用“elbow检测算法”自动找到可能与k的最佳值相对应的“肘”。但是，用户可以通过设置关闭功能locate_elbow=False。您可以在 Kevin Arvai的[Knee point detection in Python](https://github.com/arvkevi/kneed)中阅读有关此算法的实现的信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\r\n",
    "from sklearn.datasets import make_blobs\r\n",
    "\r\n",
    "from yellowbrick.cluster import KElbowVisualizer\r\n",
    "\r\n",
    "# Generate synthetic dataset with 8 random clusters\r\n",
    "X, y = make_blobs(n_samples=1000, n_features=12, centers=8, random_state=42)\r\n",
    "\r\n",
    "# Instantiate the clustering model and visualizer\r\n",
    "model = KMeans()\r\n",
    "visualizer = KElbowVisualizer(\r\n",
    "    model, k=(4,12), metric='calinski_harabasz', timings=False, locate_elbow=False\r\n",
    ")\r\n",
    "\r\n",
    "visualizer.fit(X)        # Fit the data to the visualizer\r\n",
    "visualizer.show();        # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "重要的是要记住，如果数据不是非常聚集，那么“肘部”方法就不能很好地工作。在这种情况下，您可能会看到一条平滑的曲线，而K的最佳值将不清楚。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1.2 快速方法\r\n",
    "上面的相同功能可以通过关联的快速方法来实现kelbow_visualizer。此方法将KElbowVisualizer使用关联的参数构建对象，将其拟合，然后（可选）立即显示可视化效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0XeNw2mp3vDVUzjnT7gwX1G+85oyPV0ZL4TMbN27ko48+YtasWXz44Yf86U9/avU6jx07xvbt27n++utrNHz88cctUrfb7ebWW2+lZ8+evPHGG+dcnj+oqKjgrrvuwuVy4Xa7mTp1Ko888gjp6en84hc/LlKcmZnJI488gtVqBWDJkiV88MEHaJrGrFmzGDlyJAAOh4MPPvgAk8lEUlISv//97wHqrKOap59+mrVr19KtWzeWLVvWoK66qCt/XVrmPT6LvNM5lBe5OXYsHbPZTFyPOEZfeFVLXtJs9JZVNX3QAyvqOtYXyLZa7EfRV4UGOOhw2o4Zx0ocTlu41WIvq07bkkK9aXd9XNU8M3U0C6aMOmNflUct4dKSREREEBYW5m8Zrcb+/fsZNmwY+/fvZ/jw4Y1naAE2btzI3r17a7Zbsu533nmHhISEFimrqbjd7hYpJzQ0lCVLlvDZZ5/x6aefsn79enbu3El8fDxLly5l6dKlfPzxx4SHh3PNNdcA+jx/H3zwAR988AFLly5l7dq1HDt2jOPHj/POO+/w0UcfsWzZMtxuN1988UW9dVRzyy23sHjxYp901UVd+evSsn9LDvERl3AoNZv4wfFcNP4ydq7JwlUY0iLX0mAlMBPA4bSNQzdMxQBWiz0DiHE4bYMcTlswcAOw0uG03eVw2p408vQCeqKvjfgNeiAHxt+vWlKoN+3WcMGPxmveiO6IHjF8tPsweaWtEp3ZIdm4ceNZP8BAJDMzk5///OfccsstzJw5k/T0dEA3GkOHDmX//v0cP36cWbNmMXnyZDZv3lyT9+DBg8yZM4cZM2ZgtVrJy8sjKyuLu+66C4C9e/cihCAvLw+328306dMpKyvjq6++4rbbbuPGG29k9uzZ5OXlsW3bNux2OytWrGDGjBkcP368xni6XC5sNhuLFi2iOWvoHTt2jLVr1zJz5kyf0n/yySfccsstTJ8+ndmzZwP6w3X+/PncdNNNXHvttezevfuM83/hhRfIy8urKeORRx5hwYIF3Hbbbbzxxhv1XuemYDKZiIyMBKCqqoqqqipMpjO9Uhs3bqR///707dsX0L+jUaNGER4eTnBwMBMmTGDrVj3i2+12U15eTlVVFeXl5cTFxTVax4QJE+jcuXOTdTWUv1rL6dISdh1ZQxUVxMXFcSqrnMqj3UgIv5JLxW0k9hvNypUrm3zd6sNqsW8AUhxO2wb0CMKHHE6b1eG03Wwk+Tnwb2A98L7VYk9FD8C43OG0rQeWAj+3Wuwu4BngHmN/LLCkxYTWRtO0Nv/Ztm2bZdu2bVpRUZHWHLZt26a9tPp7zfz4O9or635oVhnnk23btvlbgs+0pNZRo0bV+fnHP/5Rk+aBBx6oM829995bk8bhcGijRo3ySa/L5dLmzJmjHT58WNM0TVu7dq1ms9k0TdO0G2+8UTt16pR20003aa+88oqmaZq2fv16bfbs2ZqmaVpFRYV2/fXXaz/8oN9Tb7zxhrZo0SKtsLBQu+mmmzRN0zSbzabddtttWkZGhvbNN99oL7zwgqZpmpaXl1ej4dVXX9X++c9/apqmaffee68mpazROmPGDC01NVX7yU9+on366adnnc/s2bO1G2+88azPd999d0a6+fPna3v27NE2bdqk3X///fV/CZqmFRcXa9ddd51WUVGhaZqmFRYWapWVldr06dO11atXa5qmaaWlpVpRUdEZ579gwQJt0aJFNeVMnTpV+8tf/tLodW7quVRVVWk33nijNmbMGO2Pf/zjWfptNpv27rvv1mynpaVpU6ZM0fLy8rTS0lLttttu0x5++GFN0/R7ZcyYMdrEiRO1xx9/3Oc6MjMztWnTpjVJV2P533a8rd331C3a82/frz3/6pNnaF+zZk2N9oULFzZYtjdFRUXatm3btG3btlm0NvAsb6lPu+zjqos5ExL49Zc7WbwpjYctQ+t9G1L4ztatW9m/f39AD0D+5ptvSEtLY/58fSiK2+0mOTmZyspKiouLiY6OJj8/nwceeACAYcOGkZ+fX5M3OTmZYcOGATBkyBBWr15NVFQUZWVl5OXlceLECcaNG0dhYSH//e9/sdlsgN6iWb58OS6Xi9zc3Jr+mUOHDhEfHw/ob+6ZmZk8/vjjLFy4kLFjx56l/7333mv0HNesWUNsbCwjRow4o7VYH0FBQZSXl/OHP/yBm266iZEjR/LVV1+RkJDAlVdeCUB4eDjLly8/4/z79u3LkSNHAL3Pp7CwkIceeqjB69zUc6nWt3TpUoqKinjooYfOWPLD5XKxevVqnnjiiZr0CQkJzJs3j7lz5xIeHs7QoUMpKCigsLCQVatWsWrVKqKjo3n00UdZunQpM2bMaLCO5uhqjMLCQr7P2MD0266iW3Qf/vWXr2u0zJs3j9///vd0796doUOHYja3a0eZT3QYw9UjKowZI/rz4a7DbD6Sy6SBPfwtKeCZN28eLpeLu+++u0XK27Wr8fF1r7/+eqNp7rnnHu655x6f6ty/fz+PPfYYs2bNOmt/QkIC6enpDBgwgNDQUEB3/Q0dOhTQl3XxfjClpqYyZMgQzGYzJpOJDz/8kJkzZ3Lw4EGklLjdbgYPHsynn37K7t27WbJkCZGRkdx1110kJiaSl5dHdHQ0wcH6zzI7O5uRI0dSWFhIUFBQnfrvvPNOTp8+fdb+p556iosvvhiA7du3s3r1atatW0dFRQUlJSU8+eSTvPTSS3WWGR4ezrJly1izZg0LFixg5syZnDhxgtGjzwxsqn3+mZmZDBmiB5cdOHCA0aNH15xLfde5qefiTUxMDBMnTmT9+vU1OtatW8cFF1xA9+7dz0g7a9asmroXLVpEeHg4GzZsoF+/fsTGxgIwZcoUduzYwYwZMxqsozGak2ftpq+IHx1LVHgXkgdN5cQ1phots2bNIj4+nuTkZBYtWkTPnj19KrM906FM97yJ+o9q8aYDflaiaCvExcXhdDrxePRBnlJKNE2r6d/at28fWVlZuFwuTp8+zV//+tcao9izZ0/S0vRZDDIzM2vekAHMZjOrV6/mmmuuISoqirfeeos77rijpo6xY8cSGRnJihUr2LFjB0lJSRw9epS4uLgabYcPH2bs2LEsWrSIp59+mtzc3LP0v/feezVBCd4f7wf9E088wbp161i9ejWLFi1i0qRJZxite+65h+PHj9dsZ2RkEBERwbRp07jiiitwuVz06NGj5lwB8vLyzjp/p9NZc/6pqakIIRq9zk09l7y8PIqKigAoLy9nw4YNNS1UgC+++IJp06addZ1OnToF6C8DK1eu5OKLL6ZPnz7s2rWLsrIyNE1j48aNJCQkNFpHXTQnTzUl5fm4Y45TVFTM8F6XERocXqOlLu3Tp0/3qdz2TIdpcQFMTuzN4Ngo3t+ZwaIZ44kJC/W3JIWfufXWW9m8eTPXXXcdYWFhJCYm8tJLLyGlZOTIkezZs4cpU6Zwxx13UF5ezoMPPsiYMWMAmDFjBt9++y3Tp0+nU6dOvPjii3Tt2hWA4OBgLr30UoKDg4mMjKS8vLzGzXbLLbfw8MMP8/nnn3PJJZfQv39/IiIiiI+PJz8/nxtuuIGFCxdy5MgRrr76agYPHsyTTz7JY489xttvv01ISMtFlXk8Ho4cOXJGsMDrr7/Ojh07iIiIYMiQITz//PNUVVXxxBNPMG3aNIKDg3nkkUfOOv/777+/5vxTU1MZNerHqN76rnNTOXHiBDabDbfbjaZpXHvttTXXtbS0lA0bNrBw4cKz8s2fP5+CggKCg4N55plnCA0NZfTo0UydOpWbb76Z4OBghg0bxu233056enq9dQA8/vjjbNmyhfz8fC677DLmz5/PyJEjG8xz33338fzzz9OzZ8+z8j/4i7l0T4oh2j2Ie2bfd4aWau3Hjh0jKiqKZ555hpiYmCZft3aHvzvZfPm0RHBGNS98vVszP/6O9voG2ayyzgeBEpwxatQobejQof6W0SQC5dpq2vnRKqXUXnzxxRYpS13b5lNRWdbg8ebqba/BGa3qKhRCjBBCHBRCPNxAmt8LIda2pg5vrBMSCDKbeFO5CxUKkpKSePrpp/0to0OSmbcPV1U5AKHB7Xc8ZGvQaoZLCBEJvAqsaiDNcOCy1tJQF306R3D9sL6kZOWxIyuv8QwKhULRwhzNT2Xv0fXszlrjbykBSWu2uCqA62l42o+XgV+3ooY6mTcpEYDFm1Wr61xYsmQJCxYs8LcMhSKgKCg9wd6j6wk2hzKs99nRkorGMWla606DJIT4HZArpXyt1n4r+jxY/wEcUsor6isjJSXFgj5yu0Wo8mjctPQAp6s8LL85ifDgDhVcqVAo/ESVVsFRVwpuXPQKGUWEOfZ8VX1pcnKy83xV1tr4JapQCBEL/BR9vZa+vuZLSkoiOjq6yfWlpKScNdjx/lPBPP/1HtJMsViT/TN/W33UpbetEkhaIbD0BpJWCCy9/tDq9lSxJf1zIso6IXpdxuAevk/43Vy9xcXFpKamNjlfW8dfTY2rgB7orahPgHFCiD+fTwH3XjgEkwneVO7CZjN+/HifB/oqFB2dorJTFJfn06dLIoO6j2o8g6Je/NLiklJ+CHwIIIQYhO4q/EWDmVqYgbFRXJPUh5Uym73HCrigV6uucdMuqaysbLFZvxWK9k7XyJ5cNGQGEaGd1ZRz50hrRhUmG2HuVuBRIcRaIcTjQoibG855/pg3SZ9JQ7W6FApFa1FYepIqdyUA0WHdCDJ3qHkfWoVWu4JSyhTgCh/SZfiSrjWYPrwfcVFhvLstnRevH0dYSN3zwSkUCkVzOF1RyNZDXxDZqTOTEmacsZKxovl06KsYGhzEnPHx5JW6+GTPEX/LUSgU7YhKt4vtGV9R5XExoNsFymi1IB3+Ss41xnS9tTmtkZQKhULhG5rmYVfmKk67ChnUfSR9u/o2S7zCNzq8szWpRwyXJ/RkddoxDuYWk9C96eH2HZWf/exnZGVl+VuGQtHmSD2+ldziTLpH9SOp10R/y2l3dPgWF8DciSpIozlUL8OuUCh+pLSiiIyTe4gI7czo/pMxKxdhi6OuKHDrqIF0DQ/FsfUglW6Pv+UoFIoAJqJTDOMHX8+4gVMJCe7kbzntEmW4gLCQIH4yPp7jxeUs+0G5vnxl/vz5vPzyy/6WoVC0CVxV5Xg8+rjGblF9iApTY0NbC2W4DNTqyE1n3bp17Nixw98yFAq/4/G42X54BVsOLaPK7fK3nHZPhw/OqGZE765MGtidFTKbI/mnGdA10t+SFApFAKBpGnuznRSUHqd35wSCzC23QvX5wOG0/RmYBGjAo1aLfavXsauBFwE3sNxqsT/ndSwc+B54zmqxOxxOmwNIBk4ZSf5ktdi/aA3NqsXlxdyJiWgavL1FhcYrFArfOHJqL0fzJTHh3RnR7/KAms7J4bRdDiRaLfaLgLnAK7WSvALcClwCTHE4bcO9jv0GqL2o4dNWi/0K49MqRguU4TqD28cMJLpTCG9vScPtUUEaCv+QU5DGdwc+JL3iW7478CE5BepFqq2SW5LF/pyNhAaHM27glECczmky8CmA1WLfB3R1OG0xAA6nLR7Is1rsmVaL3QMsN9LjcNqGAsOBVjNODaEMlxeRnUKYPW4QmQWlrJA5/paj6IDkFKSxK3M1xeV5gEZxeR67Mlcr49UGqXK72HVkFZhMjB0whbCQKH9Lag69gJNe2yeNfXUdOwH0Nv5/GXi8jvIedjhtqx1O238cTlv3lhZbjTJctZg30VgdWQVpNMro0aMZMmSIv2W0K9JP7gT0CDWXdromSq16v6LtEBwUyoi+lzOi72V0jezpbzktRUN+ThOAw2mbA2y0WuyHah1/F7BZLfargJ3A71pFISo44yyS+3djbN9Ylv2QRU5RKb1jIvwtqc3yzjvvkJKS4m8Z7YqS8nyq3JWUlOfh1twUlecSHdaNkvICf0tTGOirxmuYTGZ6dh7kbznnSjY/trAA+gA59Rzra+ybBsQ7nLYbgH5AhcNpy7Ja7N94pf0M+FtriVYtrjqYO2kIbo/Gkq0H/S1F0cGICutKcFAI4aExBJs64fG4KS4/RacQ9QLVVjhwfBsph1dQ2T7C3lcCMwEcTts4INtqsRcDWC0ZbyLTAAAgAElEQVT2DCDG4bQNcjhtwcANwEqrxX671WKfYLXYJwGL0aMKv3E4bR8Z/WKgr/jxfWuJVoarDu4cO5iI0CDe3JyGx6P5W06b5b333mPFihX+ltEucHuqOHLqBwZ3HwNAeGgUIaZwwkNj8HjclLoKOV2hWl3+JqfgIOknd3C6ogBNC/wALqvFvgFIcThtG9AjCB9yOG1Wh9NWvW7iz4F/o69W/77VYk9toLjXgPcdTtu36K2yZ1tLt3IV1kHn8FBmjR7Ekq0HWZN2jMlJvRvP1AH5wx/+gMvl4le/+pW/pQQ0FVVlbM/4isKyk4zsdwWj+19F+smdFBeXEBczgMjQLpRWFhMaHO5vqR2aorJc9mR9S5A5hHEDpxIaHOZvSS2C1WK31dq1y+vYOuCiBvL+zuv/NcCEltZXF8pw1cO8iUNYsvUgizcfUIZL0WqcrihkW8ZyylzF9OmSSO8uCZhNQfTuMoSUohSSE5MB8GhuzCZ9oVOPx43ZrBY9PZ9UVJWx/fBKPFoV4wZMJTos1t+SOjTKVVgPFw3qwfCenfl0Tya5JeX+lqNoh+SfPsamg59S5iomIW4cI/tdUWOcalO9P//0Mb6V/6Gg9MT5lNqh0TQPOw9/TXllCYk9JxAXM9Dfkjo8ynDVg8lkYt6kRFxuD++mpPtbjqKdUVKez9ZDX1DldjGi72Uk9hzv04wL5ZUluKpK2XboC/JPHzsPShUmk5m+XQV9uiQS32OMv+UoaGXDJYQYIYQ4KIR4uI5jVwohNgkhvhNCvCWEaHNG9CfJ8YQGmVm86YARAqtQtAyRnbrQP3YoyYOupV/sUJ/z9e4yhNEDJuP2uNl6aDmnSo62okpFNf1iBaP6XxlQ0zm1Z1rNWAghIoFXgVX1JPk7MFNKeQkQDVzbWlqaS7fITtwyagD7TxTx3aGTjWdQKBpA0zycKDoM6C36YX0uoXt0/yaX06tzPGMHXoOGh5SMr8gtzmxpqQrgVEk2uzPX4vZU+VuKohat2cqpAK5HH7BWF8lSyurFr04C3VpRS7OZN8mYSUOtjnwW3333Hf/4xz/8LSMgcHuq2HnkG7YfXkF2/rnfS3ExAxk3cCoA+3M24WkHodltiVJXETuPfE1OQZox/ZaiLWFqbReYEOJ3QK6U8rV6jvdGHyMwUUp5qq40KSkpFiPNeUfTNGZ+fpATZZUsvzmJ6FAVzaVoGm7NxbHK76nQiggzdaFnyAiCTC0T0FvmKSDEFEawqX2EZrcFPJqb7MrtuLTTdA9OIiaoj78ltQSXJicnO/0toqXwazi8ECIO+Bx4sD6j5U1SUhLR0dFNriclJYXk5ORmKNR5sCiMp7/YwQ/uzjyYLJpdjq+cq97zRUZGBt9//z033HCDv6X4zPm+tqcrCknJ+JJQl8bALmMZ2fdyn0PZm6q1uDyPkvJ8endJaK7ccyJQ7luoX6umaew88jWdiswMiZ3IBX0tflB3Ns29tsXFxaSmNjRmODDxW0CEECIG+BL4jZRypb90+MKc8QkEm00s3qyCNLyZMWMG//M//+NvGW2W0ooiNh1cSqmriPgeYxnV78pWG39VHbK9K3MVR/Pb34PqfJF2IoXjRRnERvZmWJ96x90q/Iw/I/leBv4spfzKjxp8oldMODdc0I9d2fmkZCl/t8I3wkOjiI3szQV9LyWp14RWjUgzmcyMGnAVIUGd2JO1lsy8fa1WV3sm2BxKRGgMYwZcU++YOoX/aTVXoRAiGd04DQIqhRAz0WcMPgSsAOYAiUKIeUaW96SUf28tPefKvImJfLonk8WbDjC+f5uMI1G0EYrKcokJ747JZGbMgKvPWwh15/AeTBh8A1sPfcHeo+vxaB4GdrvgvNTdXhjcYxQDug0PxAUhOxSt9u1IKVPQZwiuj06tVXdrMEX0ZkDXSP694xAv3ZhMVKcQf0tStDE0TUMe20RG7h7GDLiaXp3jz/u4n5jwblwYrxuvfdnfYQIGKOPVIK6qco6c2kt83FjMJrMyWgFAmxv021YJMpv56YQESiqqeH9nhr/lKNoY1eHuGbl7iOzUhZjwHn7TEh0Wy8T4G4kO60bXSDXPZkN4NDc7j3xN2okUsgvUkJdAQRmuJvDTC4dgNpl4c5NaRl3xI66qcrYe+oLjRYfoGtmbSfEziAhtevRrSxLZqTMXD7mlZjLYSrdLBRbVwb7sjeSdzqFnzGD6dknytxyFj6g2cRPo3zWSqUP78OW+o+zJyWdk767+luRXXnrpJdLSOrYRL688zZb0ZZS6CundOUGfKLeNzNxe7abUNX5Or87xJPZs3SCRtk5OQRrpJ3eSXXGE3O93UF55mu5RfRnZ74oOfV0CDdXiaiLzJg4BYPEm5Va45ppruPDCC/0tw6+EBoURFhLB4B6jGdX/qjZjtM7GRPrJnezP2dRhW145BWnsylxNcXkeHq2SU8VHKa0opE/XJIKDVJ91IKEMVxOZNrwfvaLD+WfKIcoq1RxmHZXyyhIAzOYgxg++HtFrYpt9Yw8LiWRi/HQiO3Xh8Kk9/JD9XYc0Xuknd9b870H/7UaFdSVbjXsLOJThaiIhQWasFyZQUObio91H/C3Hr1x33XU89thj/pZx3jl8ai/fyv9w0pjcNhDG+3QKieDC+OlEh8WSmfcDe4+uaxdLzzeFgtKTNRPmBpvC6BzRg5CgTpSUF/hZmaKpKMPVDO69UHcXvtnB3YXZ2dnk5ub6W8Z5Q9M0ZM5m9mV/R4g5lNCgwJofsFNwOBcOnk5MeHeyCw5yuqLQ35LOC0Vluew4/DUl5XmUuYpr9leHvUeFdfGXNEUzUcEZzSChezSTE3ux6sAx5IlCRFxnf0tStDJuTxV7stZyrDCdyNDOJA++jojQGH/LajIhwZ2YMPgGSsrziQpr38FF+aePcfDEDnJL9JZxt6i+lLqKzkqnFocMPJThaiZzJyay6sAx3tycxh+nB8bEoormUVlVwfbDK8gvPUbXiF6MGziVkOCAGj9/BiFBoXSN7AnoYfKHTu5kSFxyGw4saToZubvZn7MJgK6RvUnoMZZuUX05VniQ9JM7KS4uIToslvgeY+jdZYif1foXh9P2Z2ASoAGPWi32rV7HrgZeBNzAcqvF/pzXsXDge+A5q8XucDht/YF3gSAgB7jbarFXtIZm5SpsJjeN7E+3iE68s+0griq3v+UoWhGTyYxbq6JX53jGD74+oI1WbQ6eSCH95E62H1kZ0AsmappGbklWTdBJXMxgekQPYGL8jUyMn0736H6YTCZ6dxnCJYkzie90OZckzlRGy2m7HEi0WuwXAXOBV2oleQW4FbgEmOJw2oZ7HfsN4D1560Lgr1aL/VIgDbi3tXQrw9VMOgUHMWdCPCdLKli6N6vxDIqAo8pdCUBwUAgTBk9jdP/J7W46oMSeE+ge1Z/c4ky2H14RcMbLo3nILkjjuwMfsu3Qck6VHAUgIjSa5EHX0jWyl58VtnkmA58CWC32fUBXh9MWA+Bw2uKBPKvFnmm12D3AciM9DqdtKDAc+MKrrCvQ56MFfbmqq1tLdPv6FZ5n5k5M5M/f7mPxpgPMGj3Q33LOO7feeivHjh3zt4xW4UTRYfZkrWXcwGvpGtmTkKD208ryJsgczLiBU9h55BtOFB9mW8aXJA+8ts2Pa/Jobo7mH+DQyZ2UuoowYaJPl0TC/TxjSQDSC0jx2j5p7Csy/p70OnYCqF7s7WXgYeAer+ORXq7BE0C98405nDYzMB59EnaADGCbYSAbRRmuc2BYz85YBsfxTWoOh04VM7hbx/rRLFiwgJSUlMYTBhhHTv3AD9lOzKZgKt3l/pbT6pjNQYwZeDW7jqzmeNEhUjK+5ML46W12XJqmaWxKW0pReS4mk5n+scMY3GN0QAbLtEEa+tJNAA6nbQ6w0WqxH3I4bU0qxzBYTwKPA4eB6jFFA4H+DqdtEfByYwZMGa5zZO6kITgPneCtLWk8d91Yf8tRnAOappF6fAuHTu4iNCiMcYOupUtEnL9lnRfMpiBGD5jM91nf0iN6QJszWpVuF6UVhXSO6IHJZKJP10RiK3szqPsowkIi/S0vkMlGb1lV0wc9sKKuY32NfdOAeIfTdgPQD6hwOG1ZQInDaQu3WuxlXmlr8wV6C+8Cq8V+xqr3DqetG/ALYBlwfUOileE6R2aOGshjn2zFseUgz0wZTXBQx+k2XLhwIceOHQuY5dobwuNxsydrLTmFB4kI7cz4QdcR0aljvcGbTWZG9b+yZtvtqcLtqSI02H/j1VxV5RzO3cPhU3sJDgrlMnE7ZlMQg7qP9JumdsZK4FngDYfTNg7ItlrsxQBWiz3D4bTFOJy2QUAWcANwl9Vif606s8Np+x2QYbXYv3E4bd+gB3L80/hb1yLBC7yjFr0xDNlvHE7bhMZE+/SUFUJME0I8bPyfIIRoW69jfiQiNJi7kuPJLipj+b6j/pZzXvnoo49Ys2aNv2W0CG5PFYVluXSJ6MmkhBkdzmjVRl/u4xu2pH9ORVXpea+/vPI0+3M2snb/exw8uQOz4RLsiFNVtSZWi30DkOJw2jagRxA+5HDarA6n7WYjyc+BfwPrgfetFntD82M9A9zjcNrWA7HAkjrq2wrgcNquczhtPzH+/5fDaTvgcNpu8U7TEI22uIQQfwAS0X2QrwF3AnHA/MbydhTmTRrC/30nWbz5ADeO6O9vOYomoGkeTCYzIcGduDB+GiFBYe0ucrA5mDATERrNyeIjbElfxoTB086bS66oLJeNBz9F0zyEhUQyuPuF9Isdqr6XVsJqsdfuqNrldWwdcFEDeX/n9X8OcI2P1S4ApjuctuvQx32NRXcRfuxLZl9aXJdLKW9BjzJBSvkcMM5HcR2C0X1imdC/G1/uyyar4LS/5Sh8pKgsF+eBDyku14eihIVEqYejgclkYmjvixnUfRSnKwrYkv45Za6SVquvpDyfiqoyAKLDutEjqj8X9L2My5LuYGD3Eep7aX+UWi32XPT+snetFnsJ+iBnn/DFcJUZfzUAIUQQqm/sLOZOSsSjaTi2HvS3FIUPnCw+wub0zzhdUUBh6cnGM3RATCYTotdEEnqMpdRVxJb0z+ucMulcqJ5H0HngAzJO7q6pd9ygqfSPHdquZvNQnEGYw2n7H+BaYJXDaUsEfJ47zxfDtUEI8TbQRwjxOPAtsNaXwoUQI4QQB6v7x2odu1oIsUUIsVEI8VtfBbdV7hgziMjQYN7anIbHo/zwbY0cY5BqesW3rPh+MRvTPkHTNMYOuIZ+scLf8tosJpOJxF4TSOw5nrLKYvJK6goUazr5p4+xLeNLNqR9zPGiQ8SE96BrZL3DfhTtj/vRIw9/arXYy4GpQL2x9bVptOUkpfy1EGImUIoe+rhIStmoH1IIEQm8CqyqJ8krhtijwLdCiI+klD/4KrytER0Wwu1jBvHWljS+OZDDFNHH35JanT59+lBS0nruo5aiegFBNKjUSjlVXITJZGZYn4vp2Xmwv+UFBAlx4+gW1a9FhgccOLaVgyd3ANA1opdRdt82F4KvaHkcTtsA499iYJHXvs/qzVQHvgRn2KSUduDDJmqsQI/Ff6qOMuOBPCllprFdPZVIwBou0IM03tqSxuJNBzqE4fryyy8DYgBy9QKCHjy4tUpCzCFEh8VyougwiT0bjbxVGFQbLU3TkMc207drItFh3RrNp2kaBaUnaib27REzkMKyXBLixqopmToe36F3O5nQx4wVotuhSCAdPRCwUXzpqxohhBgipUxrijopZRVQJUSdbpiGphKpl9TU5q9Uej4esEGaxpAunVj6/RG+/m4zsWHN7woMBINQTVvXml1xBKOLllBTJCa3mdLTZZSePkJKUdvW3havbZmngJzKnew9tJneIaPpZP5xxhhvvZqmcdpzggL3EVzaafqFjCfUHAWAiTjS846iO1z8Q1u8tg0RaHrrwmqx9wdwOG1/AZZYLfYdxvZE4C5fy/HlyToK2CeEOAW40C2lJqUc0HC2JuGTjyApKYno6KZPq5SSknLeBsnOL4vi0U+3sssVxZOXXNCsMs6n3nPh66+/Ji0tjZ///Of+ltIgxfslpRWFmM1BFBcX19xD0WGxJCe23evclu+DrLx+fH/0W0rNGfToPoITRRlknzxCnx4DGNR9FB7NzaGTuyhzFRFGEIO7jGNIz7FtZlqmtnxt66K5eouLi8/phb8VGWe12GuWT7da7JsdTtsLvmb2xXBNb5ashqlvKpGA567kwTy1bDtvbk7jiSuGt2u//ZNPPonL5WrThsujualyuygsO0nn8B5nHFMLCDaffrECs9nM1kPL2XboC8NlqFFUdor1qf8lPDSasJBINY+goj48Dqft94AT8AAXAz5P0eJLVGEWcBnwBPrEiBdLKQ83Q2gNUsoMIEYIMUgIEYw+lcjKcymzrdA1ohO3jh5A6ski1qWf8LecDs+BY9uo8rgY2G0EnSN6ACaiw2IZ3f+qDr8W07nSp0siESHRaBoUlZ9Cw4PJZCI0OIxgcwiXi9lc0PdSZbQUdXEbusF6AHgQCDX2+YQvLa5X0GfKWIvu0rtNCDFJSvloQ5mEEMnoU98PAiqNyMTPgENSyk/4cSoRgPellG2yPdsc5k1M5F8ph1i86QCXJ/T0t5wOy6mSbA7l7iIiNIYL46cTHBRCSlFKm3YPBhoezU1UWFeq3C7cLt27EBEagwmzmvxWUS9Wi/0E8GuH02bCx64ib3wKzpBSXu61/ZoQYn1jmaSUKegLi9V3vMGpRAKZS+PjED1i+Gj3Yf735gnERrTPtZzaMpVVFezJWosJE6P6X9nm15cKVKLCuqKVa4QGh1HsKvba38WPqhRtHWPw8a+B6qAFE3oElU8jzn0xXKFCCLOU0gNq5gxfMJlMzJ04hF8u286/UtKZf+kwf0vqcOw/tonyyhKGxCXTJUK1eluL+B5j9DFydexXKBrgXmCU1WI/0mjKOvClj+sLYKsQYpEQYhGwDWOpZ0X9zJmQQEiQmcWb0tSM1n4gIW4sA7pdQHycWiOtNendZQij+19FdFgsqv9Q0QQONNdogW8zZzwvhPgGmIjelHtASrmluRV2FHpEhTFjRH8+3HWYzUdymTSwR+OZAoylS5fy/fff+1tGnUSExjC8zyX+ltEh6N1lCL27DFH9h4qmsMfhtL2HHjtRVb3TarG/5UvmRltcQojewIVSyv+VUr4C3CiE6NtMsR2KeRP1t87Fmw74WUnrMGjQIHr3bjvzy2mah92Za8g/fdzfUhQKRcP0QZ9d6SLgUuNj8TWzL31VbwPeVnCPsT3Vd40dk8mJvRkcG8X7OzNYNGM8MWGh/pbUopSUlFBWVtZ4wvPEoZO7yS44gEdz10wvpFAo2h5Wi/2nAA6nLRbQrBZ7flPy+9LHFSal/G/1hpTyfUCFaPmA2Wzi3olDKHW5+feODH/LaXEuueQS7rvvPn/LAKCw7CQHjm+jU3AEw/v4/OKmUCj8gMNpu9jhtB0E9gOpDqdtv8NpG+9rfl8MlyaEuFYIES6EiBRC3NpstR0Q64QEgswm3myn7sK2gNtTxe4jq9HwMLLfFYQG+zwAX6FQ+Ac7MMNqscdZLfYewGyM2eJ9wRfDdR/wJPpEuDnAPPS1VBQ+0KdzBNcP60tKVh47svL8Laddsj9nE6ddhQzsNpLu0f38LUehUDSO22qx10R2GZPtVjWQ/gx8iSpMA66u3vYe06XwjXmTEvl8bxaLNx/gr/0m+ltOu6LK7eJUyVGiw2JJ6qWWKFEoAgSPw2m7BfjG2L4WcPua2Zf1uKxABPAG+urH/YUQdinl35qutWNyrehD384RvLf9EH+8YRyRnVQXYUsRHBTKxYm34KoqI8isxsUrFE3F4bT9GZiEPtzpUavFvtXr2NXAi+hGZbnVYn/O4bRFAA6gJ/rEuM9ZLfZlDqfNASQDp4zsf7Ja7F/UU+3P0BcafhN9zsJNxj6f8MVV+IBR+M3A98Bg4HZfK1BAcJCZn16YQFF5JR/savaYO4UXmqZRXqmvvhxsDlETuSoUzcDhtF0OJFot9ouAuehz03rzCnArcAkwxeG0DUdfMWSb1WK/HH1iXO++qaetFvsVxqc+o4XVYj8A3G612LtaLfZuwL1Wi/2gr7p9MVxlUsrq1Yz/a7gJ1VQQTeTeC4dgMsGbm9tPkMZTTz3F3Xff7Ze6M/P2sT71v5wsVi8CCsU5MBljJiSrxb4P6Opw2mIAHE5bPJBntdgzrRa7B1gOTLZa7O9bLfY/Gvn7o68g0iQcTttDwBKvXf92OG0P+5rfJ9+KEOKv6Bb3PiHERTRh3RSFzsDYKK5J6sNKmc3eYwVc0CvwJyG98847/bIq6+mKAvbnbCLIHOTT0vEKhaJeegHeP+KTxr4iGlmp3uG0bQD6oS9LVc3DDqftcSPtw1aLPbeeen+CPui4minAOuA1X0T70uK6CzgA3CildKMvU+KzL1LxI/Mm6TNptKdW1/nGo7nZlbkaj1bFBX0vVUtnKBQtS0NLjJxxzGqxXwzcCPzTWJ7kXcBmtdivAnYCv2ugrCCrxe4dRag1UvcZ+BJVmAP8xWv73w0kVzTA9OH9iIsK491t6bx4/TjCQnyawb/NMmfOHPLz8/n888/PW51px1MoKsulb9ckenWOP2/1KhTtlNqr0fdBH/ZU17G+QLbDaUsGThguxJ0Opy0Y6GG12Fd5pf0MaCiA7zOjxbYevQE1GfjIV9G+tLgULURocBBzxseTV+rikz2B3zeza9cu0tLSzlt9eadzSD+5k/DQaIb1vvi81atQtGNWAjMBHE7bOCDbarEXA1gt9gwgxuG0DTKMU/VK9ZcBTxh5egJRQK7DafvI6BcDfS3GemfgtlrszwO/5MfxwQ9aLfYXfBWtDNd5Zu6kRADe2nz+HvjthYjQGHpED2BUv6sIDmpf8z4qFP7AarFvAFKM1s8rwEMOp83qcNpuNpJUr1S/HnjfarGnAq8DcQ6nbT36slcPGcEbrwHvO5y2b4FpwLONVN8ZqLBa7IuAk4a70Sd8Dc4YDegL7hhIKc9ePU7RKEk9Yrg8oSer045xMLeYhO7RjWdSABAWEknyoGv9LUOhaFdYLXZbrV27vI6dtVK91WIvA+6so5w1gE+zADictj8AicBAdIN3JxAHzPclvy/LmnyE7ntcAPzW+PzGl8IVdTN3ogrSaArHizLILW5yxK1CoWi7XG612G9Bj17EarE/B4zzNbMvLa5BUspmLWcqhDhjRLaUcqvXsYfQQyLdwDYp5WPNqSMQuXXUQB79ZCuOrQd59toxhAQpj219lLlK2JO5Fg2Ny8VsNYGuQtE+qF4PSQNwOG1B+OgBBN/6uKQQoskdCkKIy4FEKeVZI7KFEDHA/wCXSiktwHAhxKSm1hGohIUE8ZPx8RwvLmfZD4HbkrjssssYO3Zsq5WvaRp7stZQ5XExtPckZbQUivbDBofT9jbQxxj3tQ59NWSf8MVwuYEfhBDvCSHeqf74kK9mRLaUch/Q1TBYAC7jEyWECEafC7FDTZ3eHlZHfvXVV3niiSdarfxDubvJO51DXMxA+nUd2mr1KBSK84vVYv81emDHKvRBzC9bLfanfM3vS9PsG36cwbcp1DsiW0pZLoR4FkhHbzL+R0qZ2liBqamNJqkXf8zw0BgjuoWzYn82X6zbRK/IMyfebYt666M1tFZ4ismu3I6ZEKoqOrM9d3uLld3Rr21rEkh6A0krBJ7ehnA4bSHAJqvF/qHDaRsNjHI4bRFWi73Ul/y+DEBeIoQYhN5xpgEpUsrmDEKqiUg0Wl6/ApLQO+dWCyFGSyl31ZcZICkpiejopkfhpaSkkJyc3OR8rc2jVZ25778b2VYWzjOXja7Z31b11uZvf/sbWVlZvPCCz8MvfGbnkW+IKowiedB19Iju32LlBsq1hcDSCoGlN5C0QvP1FhcXn9MLfyuyBPjY4bRtBD4EPkGfvPc2XzL7ElX4M2ANcAf69E9rhRD3+FB2QyOyhwHpUspcKaULfYxA4NxFLcTtYwYS3SmEt7ek4fYE3hJnr7/+Oh9//HGrlD2q35WMHTilRY2WQqFoM/S1Wuwfoq808jerxf5L9CFXPuFLH9fdwDAp5W1SypnASHybq7BmRLYQYhyQLaUsNo5lAMOEEOHG9nj0+RA7FJGdQpg9bhCZBaWskDmNZ+gAeDz6WnJmcxA9Ywb5V4xCoWgtOhkDjm8Glhn7onzN7IvhqpJSlldvSClPowdWNIiUcgOQIoSoGZEthLAKIW6WUh4H/gSsEUI4gR1SyvW+im5PzJuoz6QRyEEaLUVFVRnrUt/ncG69M8UoFIr2wVqgEMixWuypDqftMUD6mtmX4IxMIcSrwNfG9lTApz4uKWW9I7KllG+gr6rcoUnu342xfWNZ9kMWOUWl9I6J8Lckv6BpGnuPrqO8sgS35vMK3gqFIoBwOG1drRZ7vtVitzmcNrvVYi8wDi0F/uqdpqFyfGlx3Q8cBX4KWIHDxj5FCzF30hDcHo0lW31eALTdkZW/nxNFh4mN7MPg7qP8LUehULQOyxxO29UAXkYLq8V+yGqxVzqctmuARpebqLfFJYQwSSk1oBz4Y33pFOfOnWMH88vPU3hzcxrllW5yck7wRgCEqoSEhOB2n3vr6HRFIfuyNxJsDmVkvyswmXyea1OhUAQWM4F/OJy2l4GvgExjf3/gWnRv3szGCmnIVbgKuAqowpiWw8BkbAf2YlJtiM7hocwaPYglWw/y3Nd7AOizYhfPTB3dSE7/sm3btnMeW+LR3Ow2FoYc2W8y4aE+988qFIoAw2qx5wA3OJy2MeiGaphxKBO4x2qx7/SlnHoNl5TyKuPfblLKM/yNQgi1gl8LU9tnu3DlboA2b7zOFY/HTXhoNJGdutC7S0LjGRQKRcBjGCifjFRdNBicIYQwAx8LIa7ix5ZWKHpH2sjmVqgIUyYAACAASURBVKo4k2dX7OLtOvq32rrx2rlzJ6mpqec0kDM4KJTR/SejEXjj2BQKhX+oNzhDCDEb2A9cju4urEKft/A0PkYVKhrn2RW7agxUXSxcuZtnVzQ4oYjfuOeee1i4cGGz8la5XZwoOgyAyWTCbFKeZ4VC4Rv1Gi4p5b+llEnAQillkJTSbHyCpZTTzqNGRTtkX84Gth9ewbHCdH9LUSgUfsDhtHVzOG3jjf+btLaTL4mHNZ5E0VyemTqaBVPqD/9eMGVUm3UVNpdjhekczU8lJrw7cTED/S1HoVCcZxxO22xgE+Awdr3qcNrm+prflwHIh4QQ9wIb8JoxQ0qpXpVbiGrDVNtl2DU8lJ9dnOQPSa1GeWUJe4+ux2wKZnT/q5SLUKHomDwOjEZf2gTgSfTZNN70JbMvLa7bgd8CX6KHyK+iecucKBqgdsvLMrgH+WUurv/7KgrLGp1hKyDQNI09mWupdFcwrPckIjt18bckhULhHwq9lzCxWuxl+DCVYDW+LGsyuJnCFE2kuuWVnZ3N69apPPjRZv6+8QAz3lrDl/dPJjzE55Wt2yQ5hWmcOp1NXPRA+sUqD7RC0YHJdTht9wDhDqdtHHoD6aSvmRt9EgohegPPAxPQw+E3Ab+RUvpcicJ3npk6mpSUKkwmE6/dciH5pS4+2HWY299Zx0fWKwgJalIfZquyePFi9u/f73P6Xp0TqKgspU/XJDU7hkLRRnA4bX8GJqE/3x+1WuxbvY5dDbyIHlG+3GqxP+dw2iLQ+6Z6AmHAc1aLfZnD+f/t3Xd8lFW6wPHfTHrvQEIP5QgiKNiQEVBZwL62beruuLi6rrqWe/feWXUtuHeZq2vXe4XV3bFclXXtioKolAEbQTqc0AIkoSQkIY1kUub+8U7CBFImIZOZSZ7v55PPZN42jzKZZ855n3OObTDwGsbkFPuBG60We20bL/tbjLySALwEOIGbfY3Zl6/wCzCm5ngSYyzXDIx+yCt8fRHRNWFmM6/+YgplR118sqWAOQtX4/jZFMzm4PjQP+usszCbfU+kZpOZ4Rm9q9BEiFDmcNqmAaOsFvtkh9M2Bvg7MNnrkGcxJlYvAJY7nLZ3MMbwrrFa7I85nLahGBOwfwzMBV6wWuxvO5y2vwC/Bv63tdf1zFN4R1fj9iVxxWqtX/B6vkkpJUmrh0SGh/GOdRoz5y/l/3J2kxYbxZNXnhlSLZZt+78hzBzOiH5nSDGGEMHlIuB9AKvFvtXhtKU4nLZEq8Ve7nDasoESq8W+D8DhtC0CLrJa7M95nT8YyPf8Pp1jazV+hFFw0WricjhtNwL3AEkYDSI8Mfg0K5MviStOKZWptbHSoVJqEEbzUPSQuKgIPrr5Qqa/sJhnV24jLS6KB34U+BnUJ0+eTE1NDT/88EObxxRX5JNXvIG4qGSyM073eosKIYLAAMB7wtEiz7Zyz6P3LaFDQPO8bA6nbTUwCLjMsynOq2vwEJDZzus+iNE1mN/OMW3yJXE9irEg5AGMj50MwOd6e9E9UmOj+OyWGZz//Gc89Nl60mKjuG2KCmhM1dXVuFxtFwK56mvYmL8ME2YmDL6QMHNoF5cI0Qe099WyxT6rxX6eZ7Lc1x1O2/H3ADr6irrVarEv70qA4FviWoWRZUdj3LzLpf1MKvwkKymWxbfOYOrzi7nzve9Ijonk5xODs+izaWHI2vpqRvc/m8SY9ECHJIQ4USFGy6pJFkZhRWv7BgKFDqdtEnDIarHvs1rs6xxOWzhGg6bS4bTFeErbB3rOb8t8h9O2BKPYr75po9Vi92kOuXbvrHsm2X0PY02ujcAmjOT1oS8XF91vZHoin95yEYlREVjfXMWnWwsCHVKrCkpzOVieR0rsAIZnBL5bUwjRqiV41r/ylKUXWi32CgCrxZ4HJDqctmGe5HSZ5/ipwL95zukPxAPFGON7r/Fc9xqMor62/BWj4MMERHj9+KS9hSR/DjwCjMQohXRzbIb49gISfjYhK5UP51zIrPlLue6V5Sy+dQZThvcLdFgtVNaWEG6OZPzgCzCZgqeEXwhxjNViX+1w2nI896sagdsdTpsVY4Dwe8BtwJuewxdaLfZch9O2D3jZ4bStBGKA260We6PDaXsIeNXhtN0K7AFeaeel91st9pu6Gnd763G9CbyplHpYa/1wVy6ulGoxPkBr/b3XvsEY/0MigbVa69+2fhXRGkt2P/75q6lc/Y9lXPHyV3z1u5mMz0oJdFjNTsmczLD08URHxAU6FCFEO6wWu+24Teu99q2gZXl80ywXv2jlOvuBH/n4sp95EuRqWnYV+jSVoC9fhR1KqSkASqnfKKVeVkp1OO2BUmoaMEprPRmjmOPZ4w55AnhCa3020KCUGuJLwOKYS8cO4u8/O4+yoy4uXvAFO4srevT158yZw+WXX95iW/nRYtxuY8FsSVpCiDbcBjwELKYLUwn6UpzxD+A/lFJnAL8BHsZIQh1l1ubxAVrrrUqpFKVUota63HPv7Hzg5579t/sasGjp+knZlB118fv3vmf2gqWsuGMWmYmxPfLav//978nJOVZJW1p1kO92fcjg1DGMHWjpkRiEEKHHarGfVFWZL4nLrbX+Xik1F3hOa71IKXWvD+e1Nz4gA6gAnlJKTQRWaq3/2MnYhcftllM4XFXLI0s2MHv+Fyy7fSYpsVE9GkN9g4uN+V/hxk3/JJ/GEAoh+hiH0/ZHq8U+z+G0vYZxC6kFq8X+S1+u40viildKnYVReTJNKRUFdOVmium43wcCzwB5wCdKqUu11p+0dmKT3NzcLryswbtlEAo6G+8laW62jE7h7dxSLnjmI56/cCgx4f4tinj66acBuPvuuymq20ZF4wGSwgaTp/eT11xRG3xC6b0QSrFCaMUbSrFC6MXbhrWex9a6BU9IZG3xJXE9AfwNWKC1LlJKzQPe8OG89sYHFAN7tNY7AZRSXwCncmxtllaNHj2ahIQEH166pZycHCZNmtTp8wKlq/G+MdFNxJureGPtbv6yoZz3b5pOZLj/pljasGEDLpeLQSNTKdpbxcDo4Zw78sqgntYplN4LoRQrhFa8oRQrdD3eioqKk/rC392sFvtiz69jji8KcThtLwGv+nIdX5Y1WQgs9Np0n9bal8y4BKOcfr6nO7BQa13huWa9UmqXUmqU1no7MIljJZeii8xmU3OxxqKtBVjfXM1r108hrBMT4fpqf9kOfnT9OBLTYlih3yI6Ip7xgy8I6qQlhAgsh9N2FXA1MMPhtGV57YrAGB/mk/bGcS3UWv9UKbWP45pwSim01u1WAWqtVyulcpRSzeMDlFJW4IjW+j3gboyKRTPG4OaPfA1atC0izMzCX07l4gVfsHBdHimxkTx/9dndOinv/rIdrN/3JUnpsTQ2NhIRHk2Du4GKmsPERwdPSb4QIuh8hjGP4ZkYlYRNGjEK/3zSXovr957HLpeHaa3bHB+gtd5xMtcWbYuNDOeDORdw4f8s4cXVuaTFRjH34tO77fq7ita1+CrTVPa+q2gdmckju+11hBC9i2cM2CqH03aG1WKv6ep12ktcs5RqdxJXn/oiRWAkx0Sy6DcXMfX5xfzX0o2kxUVx19TuWXW4oqaEytpSElJjOFJc1by9sqasW64vhOjdTiZpQfuJq2mcVjowAfgWY2XLczBGO0viCnIDEmNYfKuRvO79YA0psZH88swRHZ/Yjrr6Wo66KnHVHyU6OhJXTGPzvvjo5JMNWQghOtTmXXut9Y1a6xuBSmCE1voqrfUVGHMXtr2WhQgqw9MS+PSWi0iJieTmhV/z4aZ9Xb5Wtaucb3a+j9kcRmR4DEOzRjF8+LFxhNkZ3dcdKYTovRxO292tbHvE1/N9KYcfqrU+2vREa12hlBrq6wuIwBuXmcJHN1/IzPmf87PXVvDpLTOYNqJ/p65RVn2ItXmf4Wqo4ZQB55IQncbu4nVUVlaREJ1Kdsbpcn9LCNEuh9N2AXAhcIPDaUv12hUJWDGmgeqQL4lrs1JqFUb3YCPGpLnbOxWtCLjJwzJ4xzqdK17+iis9k/KeMSi14xM9Sqv242qoYWzWFIaknQrA6i/Wkbe7gev+cK2/whZC9C7bOLaeY4PX9irgZ75exJfE9WtgBnAaxowXdoyJEUWImamyePUXU/jF6yu5+G9LWXHHbEZnJLZ7jtvtxmQyMSx9POkJg0iITmve9+ijj+JyufjDH/7g79CFEL2AZwb5NxxO2yqrxb6nq9fxZQCyG/jc8yNC3E9OH0bpURe/+9e3zJq/lJV3zGJQ8omzuLvdjWzb/zUAY7KmYDKZWiQtIYToLIfTttBqsf8UcDqcttbmKvRplRBfWlyil7l18mhKqmp54NN1zF7wBct+N5P0+Ojm/Q2N9azf9yWHyvOIj0qhvsFFeFhkACMWQvQS93gefwHs7epFZGnaPsp20TjumTaGrQePcNlLX1JRUwdAbf1Rvtv1EYfK80iNy+KcEVdI0hJCdJcPHU5bFDAXI3HtO+7HJ9Li6qNMJhOPXz6JkmoXr3y/k2scy3jrxolszF/CUVcFWcmjGDdoqsw9KIToTrswCjHMeK18jFE/4cYYK9whSVx9mMlkYsF151JaXcuHm/N5dPFnzBxZycj+kxjZb1K3zm8ohBBWi/0nAA6n7W9Wi/03Xb2OJK4+LjzMzJs3TuXSv33Bs6sO4GYIs0/zLWktX76cdevW9UCUQoje5GSSFkji6tPcbje7i9bT6G7gvV9PZ8b/fs5zq4qJifyBeZdN7PD85OTkLq2PJoQIHg6n7SmM8blu4C6rxf69174ZwF8wxlwtslrsj3q2Pwacj5FD5lkt9ncdTpsDY4mqw57TH7da7O2usdhVkrj6qEZ3I1sKnOSXbiM6Ip5h6afxyW8uYtrzi3nsq82kxUXx7xec2u41CgoKKCoq6qGIhRDdzeG0TQNGWS32yQ6nbQzwd2Cy1yHPArOAAmC5w2l7B+gPjPOckwb8ALzrOf6PVov9Y3/HLVWFfVB9g4u1eYvJL91GQnQa5464kvCwSDLio/ns1hkMSorlPz9ey8vftj9ByiWXXMI999zT7jFCiKB2EfA+gNVi3wqkOJy2RACH05YNlFgt9n1Wi70RWOQ5fgVwnef8MiDO4bT1aBWXJK4+pqauim93fURx5T7SEwZzTvblzetpAQxJieOzW2eQFhvFb9/+lnc3dHmohRAi+A0AvLtNijzbWtt3CMi0WuwNVou9aT2jORhdiE3TN93hcNq+dDhtbzmctnR/BS2Jq4/Zc3gTFTWHGZw6holDZ7U6RmtM/yQW3XIRsZFhXP/6Sr7I3R+ASIUQAdBeVVaLfQ6n7UqMxHWHZ9NrgM1qsV8IrKMTKxp3liSuPmZU/zMZP/hCxmZZMJva/uc/c3Aa7900HYCrHcv4fm9xD0UohOhBhRxrYQFkAfvb2DfQsw2H0zYLuB+42GqxHwGwWuxfWC32pjLjDzHmt/ULSVx9QH6pZu/hzQCYTWFkJY/0qdz9wlGZvHHj+VS7Grj0b1+y9eARf4cqhOhZS4BrARxO20Sg0GqxVwBYLfY8INHhtA1zOG3hwGXAEofTlgQ8DlxmtdhLmi7kcNre8dwXA5gObPJX0JK4ejG32832g2vYlL+c7QdzqGvo/PqfV502hPnXncvh6lpmzV/KnpJKP0QqhAgEq8W+GshxOG2rMSoIb3c4bVaH03aV55DbgDeBlcBCq8WeC/wUSAf+6XDalnl+hgDPAwsdTtty4FLA54UhO8uv5fBKqRbjA7TW37dyzDxgstZ6uj9j6Wsa3Q1syl9BYdl2YiITOHPYxUR0cc7BX58zktLqWv7j47XMXvAFy2+fSb+EGObNm8fOnTu7OXIhRE+yWuy24zat99q3gpbl8Vgt9gXAglYutRc4q9sDbIXfWlxKqWnAKK31ZIwbeM+2csxYYKq/Yuir6hpqWbP7UwrLtpMUk8G5I35MXFTySV3z3y44lf+88FRyi8q59KUvKa9x8X3YQDbFy6rHQoie5c+uwubxAVrrrUCKUur4VQufwLjBJ7rRzkM/UFJVSL/EYZydfTlR4THdct3/uuQMbj53JGvzSzj9rx8zd8kGXtpUzCOL13d8shBCdBN/dhUOAHK8njeNDygHUEpZgeVAnq8XzM3N7XIwOTk5HR8URE4m3ka3mcjG/jTWprKuuHuTypxhESzdEkleaRVxnz4HwFzupLCwkFvG9+vW1/KXUHovhFKsEFrxhlKsEHrx+lNPTvnUXMamlEoFbgJmYJRY+mT06NFdmhsvJyeHSZMmdfq8QOlKvIfK99LQ6CIz2b9dd48sXk9euVHkYa5sLijipU3FZGVl8dCsCX59/ZMVSu+FUIoVQiveUIoVuh5vRUXFSX3hD1b+7Cpsb3zAhUAGRqXKe8BETyGH6IK9hzezds9nbC5wUtdQ67fXeWTxeuYu2dDm/rlLNki3oRDC7/yZuJrHByilJgKFWusKAK31v7TWY7XW5wJXAWu11jLpXSe53W70/m/ZUriKyLBozhx+CRFhUQGNadGWfHYUlwc0BiFE7+a3xKW1Xg3kKKWaxwcopaxKqas6OFX4oKGxnvX7vmB38XriIpM4d+SPSY717z2mh2ZN4MGZ49vcnxQdwZr8Ek6xf8C1juV8nSczxwshup9f73FprdscH+B1TB7GKGvRCdsPruHAkV2kxA5g4tBZRIT3TEur6R7W8V2GD84cz/0zTuPdjXt5YtkW3tu4l/c27uW8YRncM20sV44bRJhZxrsLIU6erMcVokb0OwOzycyIfhMJM/fsP2NT8nrsO+PxwZnjm7f95PRhXDdhKCt3HeKJZVv4eEs+q/OWMyItgXumjeFXZ40gNlLedkKIrpNPkBBSVn2Q+gYX6QmDiQiLYvSAswMWi5GoHqSwsPCESkKTycTUEf2ZOqI/2w4e4akVW3htzS7uePc7HvpsPbdNGc3vpij6J3TP+DIhRN8ifTch4uCR3Xy362PW7f3Cr5WDnfHQrAkdjt06pX8S86+bzO4HruZPPzLuj/35840M//O73PLPr2XiXiFEp0niCnJut5u84g38sPdzTCYTE4ZcGPDKwSbz5s3jlVde8enY/gkxPDx7Anl/uprnrzmbwclxvPztDsY99iFXvPwly3cexO12+zliIURvIF2FQcztbmTb/q/Zc3gzUeGxTBo2m8QYvy0q2mlvvfUWLlfnZpyPjQzntvMUt5w7ig835/PUsi18sqWAT7YUMGlQKvdOH8u144cSHibfqYQQrZNPhyC2/eAa9hzeTHxUCueO+HFQJa2TFWY2c9VpQ1hx52ycd87m6vFDWFtQwvWvOxk9732eWbGVipq6QIcphAhC0uIKIvvLdrCraB2FtXup2b6bQaljGJgymlMyz+vykiShYPKwDN4eNo2dxRU8vWIrju93cO8Ha3hk8XpumTyaO88/hYFJsYEOUwgRJKTFFST2l+1g/b4vKas+RKO7joqaErYWriI9flCvTlreRqQn8NzVZ5P3wDU8evHpREeE8fhXm8n+87tY31zFhsLSQIcohAgCkriCxM5D66ipq6L8aBEudxVudyMAu4rWBTiynpcWF8V9M05j1/1Xs+An5zIqI5HX1uzijCc+Ztb8pSzRhVLIIUQfJl2FQaCqtozCsu3UN9RiMpmJMMVgMhnfKSprygIcXdsyMjKoqqry2/WjI8KYc84objprJJ/pQp5ctpmluftZmruf0zKTuXf6WH52+jAiw8P8FoMQIvhI4gqgRncjeUUb2HEoB7e7kYjwaOIik6iqqm4+Jj765FYu9qelS5f2yBpBZrOJS8YM5JIxA8nZd5gnl2/h7fV7uOnN1dz/yQ/8/vwx/GbyKJJj+kaXqhB9nXQVBpAJOFi+m/CwSMYNmkpCdCpmc8vWQ3bG6YEJLkhNGpzG/91wPjvuu4p7po2hvLYO2ydrGfroO9z7wffsKals89xHFq9nwYZDPRitEMIfpMXVwxoa6ymtOkB6wiBMJjMThlxEhDmKiPAoUuMy2VW0joqKShKiU8nOON3vC0OejGXLlrF9+/aALMg3JCWOv15xJg/8aDwvfbOdZ1du45kV23jeqbl2/FDunT6WMwenNR/vvZZY1uL1Qb/gpRA9xeG0PQWcC7iBu6wW+/de+2YAfwEagEVWi/1Rz/bHgPMxcsg8q8X+rsNpGwy8BoRhrL14o9Vi98s0P9Li6kGlVQdYtf0dcvI+pfzoYQBiIxObZ3bPTB7JlFHXkh01jSmjrg3qpAVw11138eSTTwY0huSYSP79glPZcd+PeeUXUxg3IJmF6/I45+lFXPQ/S/h4Sz4Pf7auxWz2suClEAaH0zYNGGW12CcDczCWoPL2LHANMAWY6XDaxjqctguAcZ5zZgNPe46dC7xgtdjPB3YAv/ZX3JK4ekB9g4stBU6+3fUh1a4jDEk7ldjIxECH1atEhodxw6Rscu69lMW3zmCmymLZzoNc+fJXPPr5xhOOl+QlBAAXAe8DWC32rUCKw2lLBHA4bdlAidVi32e12BuBRZ7jVwDXec4vA+IcTlsYxvJUH3q2fwTM8FfQ0lXoZ0UVe9lc4KSmrpK4qGTGDZxGSlz/QIfVa5lMJmaMzmTG6Ex+969vmP/19jaPbWqFSbeh6MMGAN4VVkWebeWeR+/VYA8BI6wWewPQVE48B6MLscHhtMV5dQ0eAjL9FbQkLj87eCSP2rpqRvSbyIiMM04ovhD+48uyKc+t3MamA2VMyEphfGYKE7JSGJISh8lk6oEIhQg67b3xW+xzOG1XYiSumZ28zkmTxNXN3G43JVX7SY3LxGQyoTLPYUjaqSTGpHV8suhWba3W3GRYShzltXW8u2Ev727Y27w9OSaS8ZnJjM9KYXxWChOyUjl1QBIxEfLnInqdQoyWVZMsjMKK1vYN9GzD4bTNAu4HZlst9qa1iSodTluM1WI/6n2sP8hfYjeqqatiS6GTQ+V7GD/oArJSRhERFkVETHAsQ9IXtZW8mlZtdrvdFBypZn1hKRsKS5sfnbuLWLHrWOm82WRC9UtsbpUZCS2FzMQYaZ2JULYEeASY73DaJgKFVou9AsBqsec5nLZEh9M2DMgHLgOudzhtScDjwAyrxV7ida2lGIUcr3seP/NX0H5NXEqpFmWWWuvvvfZdAMzDKLPUwM1a60Z/xuMvbreb/FKN3v8N9Y0uUuIySYptf4HF3uDtt99m8+bNgQ6jQ8cnr6akBcY9sUHJcQxKjuPSsYOaz6l21bPpQFlzIttQWMqG/aVsPXiEhevymo9Lj4tifGaKV+sshbH9k056No9HFq+nsPAQ83t+pIHoQ6wW+2qH05bjcNpWA43A7Q6nzQocsVrs7wG3AW96Dl9otdhzHU7bLUA68E+H09Z0qV8CDwGvOpy2W4E9gG+L9XWB3xKXUmoaMEprPVkpNQb4OzDZ65AFwAVa63yl1NsYZZWL/BWPv1TXlrOpYAUlVYWEmyM4deD5DEo5pU98Cx89ejQVFRWBDsMnTYmqsLDQp2KM2Mhwzh6SztlDji0l43a7ySupPNY62288frnjAF/uONB8XLjZxJj+SUYiyzyW0Pr5cM8NZMyZ6FlWi9123Kb1XvtW0PJzG6vFvgDj87s1P+re6FrnzxZXc5ml1nqrUipFKZWotS737J/k9XsREJI3gYoq91FSVUhGwhBOHWghOiI+0CH1GJfLRV1d6KyZ9dCsCeTk1Hf5fJPJxPC0BIanJfDj04Y0by+vcbFxf1mLrsaNB0rZuL+M/2N383EDEmKak1jTo8pIbLFopnfSAql8FKI1Jn/Nsq2UWgB8orX+wPN8JTBHa5173HGZwErgHK314daulZOTY/EcExRcjVWEm6Ixm8Jwu90cdZcSY0rpE60sbzfccAMAr7/+eoAjCT4NjW7yK11sL6tle2mN8VNWw8Hqlokz0mwiOymKUSnRFFXX8c2B1ictvnlcOreM7/3dz8Jvzp80aZIz0EF0l54szjjhU10p1Q9joNrv2kpa3kaPHk1CQkKnXzgnJ6dbpiVqbGxgZ9EP7CrKZWjaOE7JPPukr9ma7orX3yIjI3G5XCERa5Oe/H/b2rujpLq2+Z7Zes99s80HythWWtPutV7aVExUUhpPXHkmEWHBOW9AqLxvIbRiha7HW1FRQW5ubscHhhh/Jq72yixRSiUCnwL3a62X+DGOblFWfYhN+cuprC0lOiKOtPisQIckQlBqbBTTRw5g+shjfxr1DY3c9f73vLi6/Q+YF1ZpXvw6lyHJcYxIT2BEWgIj0xM8v8eTnZZAbKQUCovez5/v8uYyS6XURKBQa+19J/8J4Cmttd9KJrtDfWMdOw6uIa/YmDZocOpY1ICzCe8jqxIL/wsPM/PCNefQLz66zTFn00f0Z3BKHLuKK9hxuMJYl+zY98BmWYkxjExPINuT1LK9kps/l32RKkjRk/yWuLTWq5VSOUqp5jJLpZQVOAIsxiifHKWUutlzyhta67YqVQKm4mgJecUbiY1MYtygqaTG+W0WE9HHdTTmzFtlbR07D1ews7iSncUVnt+Nx5W7D7UYg9YkLTbKk8ziGZmeSHZ6PCPTjKTWLz66y/dopQpS9DS/9itordssswSCdlRuXX0tDe56oiPiSInrzxlDZ5IeP4gws3TDCP9qb8yZt/ioCCZkpTIhK/WEfbX1Dew+XNkime0ormDX4UrWFpTw7d7iVq4Xzoi0hOYuyBHpnpZaWgKDkmIxm1tPalIFKQJBPomPc/DIbrYUOomPTuXMYZdgMpnonzgs0GEFpXvvvZe9e/d2fKDolM6OOTteVHgYp/RP4pT+SSfsa2hsZF9ZNTu8WmlGUqsgt6ic9YWlrVzPzPDU+Bb31bLTEvh0awEvrNInHC/JS/ibJC6P2rpqthSu4mD5bsymMFLjsjAm/OhbJe6d8atf/YqcnJyODxSddrJjztoSzrLmXAAACrpJREFUZjYzLDWeYanxzDhu8m63282BiqNGUiuuZOfhcs+jkdy2HSpv46onmrtkA1sPHuG2KYr0uCjSYqNIi4sKiopIuR8X+vp84nK73RSWbWfb/q+pa6glObY/4wZOIz46OdChCdGjTCYTmYmxZCbGcn72iUvvlFTXNrfQXvpmO8t2Hmz3em+v38Pb6/e02JYYHdEikaXFRXk9j27xPN2zP+okp8/yJvfjeoc+n7hcDTVsLVyFGzdjMs9jSNqpfW4gcVfNmTOH0tJS3n333UCHInpAamwUqUOiOGtIOj+fOPyE+1vefjxuMFNH9OdwVS3FVbUcrq7lcFUNh6tcFFfVsL6wFFeDb1OTxkeFNyeyVK+EZmyLJjUukvS46BbJrrVhAXI/rvfok4nL7XZTU1dJTGQCUeExTBhyEfFRKcREdn5wc1+2Zs0aXC5XoMMQAdKZKsjjud1uqlz1LRJbcVUtJS2e13C4qtb4qa5ly8EjHK1r8Cm2mIiwFolsf/lRthw8csJxc5ds4MhRF3++5IygHQMnXZsnCs5/KT+qrCllU8EKauqqsIy6jvCwCDIShnR8ohDiBL5WQR7PZDIRHxVBfFQEQ1N9n9+z2pPsmhKdd2JrSnTFVbWUePbvOFzBulYKTrw9s3Ibz6zcRkxEGOlxRiuuqcsyIz66Ofmlx0V79h/rzjzZVQA6Il2brevViWt/2Q52Fa2jsHYvR3N3EROZQHFlPm53IwOSsml0NwARgQ5TiJB2slWQnREbGU5sZDiDU+J8PudPn/7AX5ZuaveY7LR4UmIiKa6qJbeonKoC3wpjmu7ZGcntuMTW4nm0p6szkjCzbwUq0rXZtl6buPaX7WD9vi8BaHTXU1CaS0NjHclx/Zk0dDb9k4YHOEIheg9/VUF2h0cvPoNws7nN+3GttRKP1h3rxjR+atp8XlRZw7oC3+7ZmUyQEhPZnMjSjktsTc8/2LSPf3y384TzJXkZem3i2lW0zvjFDXXuasyNEBURS0JUqiQtIfqYzt6Pi4kIZ1ByOIOSfWvZud1uKmvrKa6q8UpuRkFKW893Hq6gobHzq3NI8urFiauyxtOvbYIIUwyxMbFEhEVR7QqNhQ9DweTJkzl8uMNJ/YUICl29H+cLk8lEQnQECdERDE/zrcirsdHNkRpXi1ZccVUtb63dzdLtBzq+QB/WaxNXfHQKFTUlAJhN4USERXm2y/is7vLiiy/KAGQRUnryflxHzGYTKbFRpMRGMSrj2Pabzh7Z7lCD7ky4oSrww9j9JDvj9E5tF0L0DQ/NmhD0i3I+NGsCD84cf8J2SVqGXtviykweCRj3uioqKkmITiU74/Tm7eLkvfTSS+zduzekFuQTIlT4s2sz1PXaxAVG8spMHklOeQ6TRsmHa3d77rnncLlczJ07N9ChCNErBVPXZjDp1YlLCCFCnb+HGjictqeAczFmFb/LarF/77VvBvAXoAFYZLXYH/VsHwd8ADxltdif92xzAJOApoqtx60W+yf+iFkSlxBC9FEOp20aMMpqsU92OG1jgL8Dk70OeRaYBRQAyx1O2zvAHuA54ItWLvlHq8X+sZ/D7r3FGUIIITp0EfA+gNVi3wqkOJy2RACH05YNlFgt9n1Wi70RWOQ5vha4BCgMTMjS4hJCiL5sAOA9pqXIs63c81jkte8QMMJqsdcD9Q7n8QvcA3CHw2m713PsHVaL/cTltrtBqCSuCIDq6uouX6CiIrQGHodCvCNHjqS+vj4kYvUWSvGGUqwQWvGGUqzQtXi9PjN9nZS1vTWdOlrv6TXgsNViX+dw2mzAw8AdPr5up4RK4joFID8/v8sXyM3N7bZgekIoxHvfffcBoRGrt1CKN5RihdCKN5RihZOO9xTgq1a2F2K0rJpkAfvb2DeQdroHrRa79z2vD4H/7VKkPgiVxPW653EbUBfIQIQQIoREYCSt19vYvwR4BJjvcNomAoVWi70CwGqx5zmctkSH0zYMyAcuA65v64U8hRt/sFrsu4DpQPtT8p8Ek9vd+UkehRBC9A4Op80OTAUagduBM4AjVov9PYfTNhX4b8+h71gt9r86nLZJwBPAMIyGRAFwNTABeAyoBiqBm6wW+yF/xCyJSwghREiRcnghhBAhRRKXEEKIkCKJSwghREiRxCWEECKkhEo5fJcppR4Dzsf4b52ntX43wCG1SikVCziA/kA08KjW2u9zfp0spVQMRtnro1prR4DDaZVSajrwNrDZs2mj1vrOwEXUMaXU9cB/APXAg1prv0xW2h2UUnOAG702nam1jg9UPO1RSsUDrwIpQBTwiNZ6cWCjap1Sygy8CIwDXMBvtdbbAhtVcOjViUspdQEwTms9WSmVBvwABGXiAi4H1mitH1NKDQU+B4I+cQEPACWBDsIHy7XW1wY6CF943qsPYcy0HY8xziZoE5fW+mXgZQCl1DTgJ4GNqF1WQGut/6iUygK+xDPBQRC6EkjSWp+nlBoBPIMxlqrP69WJC1gBfOf5vQyIU0qFaa0bAhhTq7TWC72eDsYY8BfUlFKnAGMJ4g/VEDUDWKq1rgAqgFsCHE9nPEg7g1SDQDHQtLRwiud5sBqF5/NLa71TKTU0WD+/elqvTlyef+Aqz9M5wKJg/0dXSq0GBhEa36yewJiL7FeBDsQHY5VSHwKpGN1Dnwc6oHYMA2I98aYAD2utW1tCIqgopc4C9mmtDwQ6lrZord9SSlmVUjsw/t9eGuiY2rERuEcp9TQwEsgG0oGDAY0qCPSJ4gyl1JUYicsvEz52J631ecAVwOtKqY4mtQwYpdQvga+11rsDHYsPtmN0t12JkWRfVkpFBjakdpmANIzZCKzAP4L5veDlZoz7tEFLKXUDsFdrPRK4EHg+wCG1SWv9KUaLawVwN7CVjie67RN6dYsLQCk1C7gfmK21PhLoeNqilJoEHNJa79Nar1NKhQMZGMsDBKNLgWyl1GUYLcRapVS+1nppgOM6gda6AGjqit2plDqAMWFosCbdg8BqrXU9RrwVBPd7ocl0IKiLXoApwGIArfV6pVRWMHe/aa0faPpdKbWT4H8P9IhenbiUUknA48AMrXWwFxBMBYYCdyul+mPclA/a/net9U+bfldKPQzkBWPSguYKvUyt9V+VUgMwKjcLAhxWe5YADqXUf2N0ZwX1ewHAU+hQqbV2BTqWDuwAzgHe8RRBVQZr0lJKTQDu0lr/Wik1G1irtW4MdFzBoFcnLuCnGH3C/1RKNW37pdZ6b+BCatOLGF1YK4EY4HZ5k3abD4E3PF3GkcBtwfwBq7UuUEr9C/jGs+nOEHgvZBIarYH5wN+VUssxPv9+G+B42rMRMCulvgNqCO6ilx4lk+wKIYQIKX2iOEMIIUTvIYlLCCFESJHEJYQQIqRI4hJCCBFSJHEJIYQIKZK4hDgJSqnpSilnoOMQoi+RxCWEECKkSOISopsopcYrpTYqpQYFOhYhejNJXEJ0A0+yehW4Tmsd9EvSCBHKevuUT0L0hARgEfAnWaFWCP+TFpcQJ28YsBRj7ST5mxLCz+SPTIiTt1FrfS/GjPP3BzoYIXo7SVxCdJ/bgBuVUucFOhAhejOZHV4IIURIkRaXEEKIkCKJSwghREiRxCWEECKkSOISQggRUiRxCSGECCmSuIQQQoQUSVxCCCFCyv8DyEWaWHHEwDEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\r\n",
    "from yellowbrick.cluster.elbow import kelbow_visualizer\r\n",
    "from yellowbrick.datasets.loaders import load_nfl\r\n",
    "\r\n",
    "X, y = load_nfl()\r\n",
    "\r\n",
    "# Use the quick method and immediately show the figure\r\n",
    "kelbow_visualizer(KMeans(random_state=4), X, k=(2,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 2 Silhouette Visualiz\n",
    "当数据集的实际情况未知时，使用轮廓系数来计算聚类密度。通过平均每个样本的轮廓系数来计算得分，计算为每个样本的平均簇内距离和平均最近聚类距离之间的差，用最大值归一化。这将产生一个介于1和-1之间的分数，其中1是高度密集的簇，而-1是完全不正确的聚类。\n",
    "\n",
    "\n",
    "|可视化器|SilhouetteVisualizer|\n",
    "|-|-|\n",
    "|快速使用方法|silhouette_visualizer()|\n",
    "|模型|聚类|\n",
    "|工作流程|模型评估|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2.1 基础使用\n",
    "红线表示平均Silhouette分数，纵坐标表示当前特征的分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\r\n",
    "\r\n",
    "from yellowbrick.cluster import SilhouetteVisualizer\r\n",
    "from yellowbrick.datasets import load_nfl\r\n",
    "\r\n",
    "# Load a clustering dataset\r\n",
    "X, y = load_nfl()\r\n",
    "\r\n",
    "# Specify the features to use for clustering\r\n",
    "# 指定某五个特征\r\n",
    "features = ['Rec', 'Yds', 'TD', 'Fmb', 'Ctch_Rate']\r\n",
    "# 挑选这些特征重tgb>20的数据\r\n",
    "X = X.query('Tgt >= 20')[features]\r\n",
    "\r\n",
    "# Instantiate the clustering model and visualizer\r\n",
    "model = KMeans(5, random_state=42)\r\n",
    "visualizer = SilhouetteVisualizer(model, colors='yellowbrick')\r\n",
    "\r\n",
    "visualizer.fit(X)        # Fit the data to the visualizer\r\n",
    "visualizer.show();        # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2.2 快速方法\r\n",
    "上面的相同功能可以通过关联的快速方法silhouette_visualizer来实现。此方法将使用关联的参数构建Silhouette Visualizer对象，将其拟合，然后（可选）立即显示它。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\r\n",
    "\r\n",
    "from yellowbrick.cluster import silhouette_visualizer\r\n",
    "from yellowbrick.datasets import load_credit\r\n",
    "\r\n",
    "# Load a clustering dataset\r\n",
    "X, y = load_credit()\r\n",
    "\r\n",
    "# Specify rows to cluster: under 40 y/o and have either graduate or university education\r\n",
    "X = X[(X['age'] <= 40) & (X['edu'].isin([1,2]))]\r\n",
    "\r\n",
    "# Use the quick method and immediately show the figure\r\n",
    "silhouette_visualizer(KMeans(5, random_state=42), X, colors='yellowbrick');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 3 Intercluster Distance\n",
    "簇间距离图在保留到其他中心的距离的情况下，在2维中显示簇中心的嵌入。\n",
    "例如，可视化中元素离中心越近，它们在原始特征空间中的距离就越近。\n",
    "根据评分度量调整群集的大小。\n",
    "默认情况下，它们的大小取决于membership，例如，属于每个中心的实例数量。\n",
    "这让人感觉到集群的相对重要性。\n",
    "但是，请注意，由于两个簇在2D空间中重叠，这并不意味着它们在原始特征空间中重叠。\n",
    "\n",
    "\n",
    "|可视化器|InterclusterDistance|\n",
    "|-|-|\n",
    "|快速使用方法|intercluster_distance()|\n",
    "|模型|聚类|\n",
    "|工作流程|模型评估|\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 3.1 基础使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\r\n",
    "from sklearn.cluster import KMeans\r\n",
    "from sklearn.datasets import make_blobs\r\n",
    "\r\n",
    "from yellowbrick.cluster import InterclusterDistance\r\n",
    "\r\n",
    "# Generate synthetic dataset with 12 random clusters\r\n",
    "X, y = make_blobs(n_samples=1000, n_features=12, centers=12, random_state=42)\r\n",
    "\r\n",
    "# Instantiate the clustering model and visualizer\r\n",
    "# 六个簇类\r\n",
    "model = KMeans(6)\r\n",
    "visualizer = InterclusterDistance(model)\r\n",
    "\r\n",
    "visualizer.fit(X)        # Fit the data to the visualizer|\r\n",
    "visualizer.show();       # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 3.2 快速方法\r\n",
    "上面的相同功能可以通过关联的快速方法intercluster_distance实现。此方法将InterclusterDistance使用关联的参数构建对象，将其拟合，然后（可选）立即显示它。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.datasets import load_nfl\r\n",
    "from sklearn.cluster import MiniBatchKMeans\r\n",
    "from yellowbrick.cluster import intercluster_distance\r\n",
    "\r\n",
    "\r\n",
    "X, _ = load_nfl()\r\n",
    "intercluster_distance(MiniBatchKMeans(5, random_state=777), X);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 4 参考\n",
    "[https://www.scikit-yb.org/en/latest/api/cluster/elbow.html](https://www.scikit-yb.org/en/latest/api/cluster/elbow.html)\n",
    "\n",
    "[https://www.scikit-yb.org/en/latest/api/cluster/silhouette.html](https://www.scikit-yb.org/en/latest/api/cluster/silhouette.html)\n",
    "\n",
    "[https://www.scikit-yb.org/en/latest/api/cluster/icdm.html#yellowbrick.cluster.icdm.InterclusterDistance](https://www.scikit-yb.org/en/latest/api/cluster/icdm.html#yellowbrick.cluster.icdm.InterclusterDistance)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PaddlePaddle 1.8.0 (Python 3.5)",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
